Information has quickly become voluminous over the past half century with improved technologies to produce and store increased amounts of information and data. The Internet makes this point particularly clear. Not only does the Internet provide the means for increased access to large amounts of different types of information and data, but when using the Internet, it becomes clear how much information has been produced and stored on presumably every possible topic, including typical sources such as articles, newspapers, web pages, entire web sites, white papers, government reports, industry reports, intelligence reports, and newsgroups and recently more prevalent sources of information such as web blogs, chat rooms, message exchanges, intercepted emails, and even transcriptions of intercepted phone conversations—essentially anything that is in written language form, or capable of being translated into, described, or otherwise represented by written language such as video, images, sound, speech, etc., and particularly those materials which are available in electronic format, such as text, images, and sound available online on the Internet, as well as from other sources. While one problem produced by this large amount of information is the ability to access a particular scope of information, another significant problem becomes attempting to analyze an ever-increasing amount of information, even when limited to a particular domain. A further problem becomes trying to predict, revise, and confirm hypotheses about events and changes in view of vast amounts of information, and identifying and organizing informational evidence to support any such hypotheses or justify any conclusions and decisions related to and based upon such hypotheses. And even further problem becomes trying to accurately employ temporal relationships to the vast amounts of information to facilitate strategic decision support.
Analysts are presented with increasing volumes of information and the continued importance to analyze all of this information, not only possibly in a particular field of study or domain, but possibly also information from additional domains or along the fringes of the focus domain. However, in a domain where the information available is beyond the amount humans can potentially process, by hand or otherwise process manually, particularly in domains involving socio-economic and political systems and of strategic and competitive nature requiring strategic reasoning, decision makers and analysts can be prevented from fully understanding and processing the information.
Even before the quantity of information becomes an issue, it takes time for an analyst to compose a framework and understanding of the current state of a particular domain from documents that describe the domain. Particular issues are increasingly complex and require a deep understanding of the relationships between the variables that influence a problem and the timing related to those influences. Specific events and past trends may have even more complex implications on and relationships to present and future events. Analysts develop complex reasoning that is required to make determinations based upon the information available and past experience, and decision makers develop complex reasoning and rationale that is required to make decisions based upon the information and determinations of analysts and the intended result. These factors make it difficult for analysts and decision makers to observe and detect trends in complex business and socio-political environments, particularly in domains outside of their realm of experience and knowledge. Similarly, these factors make it difficult for analysts and decision makers to “learn” or “gain understanding” about a specific topic by synthesizing the information from large number of documents available to read. As opposed to, for example, engineers, physicists, or mathematicians who generally learn the concepts of their field by using the language of mathematics, in areas such as history, political science, law, economics, and the like, the medium in which to learn concepts is the use of “natural language” such as English. For the most part there are no formulas or like logic rules which can be established and followed. Even the semantics to capture and convey the domain knowledge are a complex challenge or unknown. Thus, it may become particularly challenging for an analyst or decision maker entering a new or modified domain and needing to “come up to speed” on the domain by, typically, reading huge amounts of material on top of merely understanding the domain. And analysts and decision makers have a limited amount of time to become familiar with, understand, and be able to analyze and/or make decisions based upon the new domain, making it difficult to make important decision based upon the analyst's or decision maker's ability to process all of the information and even more difficult to accurately predict timing of possible future events based upon known information.
Further burdening analysts and decision makers, increasing amounts and complexities of information available to analysts and decision makers require significantly more time to process and analyze. And much needed information to predict trends may be found in streams of text appearing in diverse formats available, but buried, online or in other sources. Thus, analysts may be forced to make determinations under time constraints and based on incomplete information. Similarly, decision makers may be forced to make decisions based on incomplete, inadequate, conflicting or, simply, poor or incorrect information or fail to respond to events in a timely manner. Such determinations and decisions can lead to costly results. And a delay in processing information or an inability to fully process information can prevent significant events or information from being identified until it may be too late to understand or react. An inability to accurately predict timing of possible future events can further degrade decisions made by analysts.
No tools are known to be available at present for accurately capturing the predictive knowledge and expertise of an analyst or domain expert directly in a simple and straightforward manner related to event timing correlated to relationships between causal nodes. To provide useful analyses of information, the model must be able to employ predictive accuracy in temporal relationships. No simple tools are known to be available to use advanced probabilistic models to reason about complex scenarios and event timing to facilitate strategic decision support.